Accuracy Is Not Enough: Designing Healthcare AI Around Error Costs

A model metric tells us how often a system is correct.

It does not fully describe the consequences when the system is wrong.

Healthcare AI design should consider:

Error → Consequence → Detection → Escalation → Recovery

A false positive and false negative can have very different implications depending on the clinical context.

For agentic AI, risk-based boundaries are essential. Systems should have clear conditions for continuing, pausing, escalating, and requiring human review.

The goal is not perfect automation.

It is safer automation.

I am open to remote roles globally.